Machine Learning Algorithms for Disease Diagnosis using Medical Records: A Comparative Analysis

The development of machine learning algorithms has revolutionized the medical data categorization industry through the introduction of artificial intelligence. The development of mathematical models employing statistical theory to draw conclusions from samples has proven to be a fruitful application of machine learning. Due to the enormous amounts of patient data, machine learning algorithms have been implemented into the medical industry to make crucial diagnostic decisions. Particularly for busy institutions, accurate diagnosis can be difficult and time-consuming. By analyzing medical imaging data and learning from labelled examples, machine learning algorithms have demonstrated tremendous potential for automatically classifying and identifying diseases. This study evaluates the performance of four prominent machine learning algorithms in various medical contexts, including cardiac care, trauma units, breast cancer diagnosis, etc. Using the Heart Disease UCI dataset as an example, this study evaluates the accuracy of various cardiovascular disease prediction algorithms using accuracy ratings and confusion matrices. It insinuates that machine learning algorithms, such as logistic regression, random forest, deep neural networks, and gradient boosting, have the potential to improve healthcare decision-making processes and demonstrates the importance of machine learning algorithms in this field.